Background: The classification of active compounds based on their function using machine learning is essential for predicting the function of new active compounds quickly. These classification results are beneficial to accelerate the work of laboratory assistants in identifying the function of active compounds. In this study, an active compound is represented by the Simplified Molecular-Input Line-Entry System (SMILES) code.

Objective: This paper proposes a modified acceleration coefficient to improve the PSO-ELM performance for predicting the function of the SMILES code.

Methods: The research uses a machine-learning algorithm that is a combination of the Particle Swarm Optimization and Extreme Learning Machine (PSO-ELM). ELM is used to classify the SMILES code, while PSO is used to optimize ELM parameters, i.e., weight, bias, and the number of hidden neurons. The important parameters that significantly influence the PSO performance are acceleration coefficients. The acceleration coefficients, that are modified Sigmoid-Based Acceleration Coefficient (SBAC), are introduced and compared with seven other acceleration coefficients.

Results: The experimental results show that the sensitivity, specificity, accuracy, and Area Under the Curve (AUC) of the proposed acceleration coefficients outperform all other acceleration coefficients. The increased accuracy of the proposed can reach up to 2.64%, 5.84%, 7.93%, 8.44%, and 16.29% for Support Vector Machine (SVM), decision tree, AdaBoost, MLP Classifier, and Gaussian Naïve Bayes algorithms, respectively.

Conclusion: The acceleration coefficients affect the prediction accuracy of the SMILES code classification. The proposed acceleration coefficients improve the performance of the PSO-ELM for predicting the function of the SMILES code.